Knowledge Graphs: What They Are and Why They Matter

Knowledge graphs are rewriting how information is organized, accessed, and understood. From powering smarter search engines to enhancing business decision-making, knowledge graphs have rapidly become foundational tools for organizations seeking to leverage data more effectively.

In this guide, we'll cover what knowledge graphs are, their essential building blocks, why they’re valuable, and how they’re used.

What Are Knowledge Graphs?

A knowledge graph is a structured representation of information, where entities (like people, places, or things) are connected by relationships.

Imagine a map of concepts, where each point ("node") represents an entity and each line ("edge") shows how they're related. These connections create a web of knowledge, enabling machines and humans alike to gain deeper context and actionable insight from raw data.

The term rose to prominence when Google introduced its own Knowledge Graph in 2012, transforming search results by revealing direct answers and connections instead of just a list of links. Knowledge graphs are not just for tech giants; they’re now being adopted in fields such as healthcare and finance.

Components of a Knowledge Graph

Understanding the parts of a knowledge graph helps explain both its utility and complexity.

At its core, every knowledge graph includes the following components:

1. Entities (nodes)

Entities (or nodes) are the objects or concepts being described. These can be people, companies, products, events, locations, and more. Each entity has a unique identifier, such as a name or label, and can have multiple properties and relationships with other entities.

The purpose of these nodes is to represent real-world objects in a structured and organized way, making it easier for machines to process and interpret the information.

Examples: "Albert Einstein", "Apple Inc.", "New York City"

2. Relationships (edges)

Relationships, also referred to as edges, are connections or associations between two nodes in a graph database. They represent how entities are related and interact with each other.

Similar to nodes, relationships can also have properties that provide additional information about the connection.

Relationships express how entities are connected. These can indicate hierarchy, association, causality, or any meaningful linkage.

Types of relationships:

• Directed: This type of relationship has a specific direction from one node to another. For example, "John works at Google" is a directed relationship where John is the employee and Google is the employer.
• Undirected: In this type of relationship, there is no specified direction between two nodes. For instance, "John and Mary are friends" is an undirected relationship where both John and Mary are friends with each other.

In addition to these types, there are also complex relationships that involve multiple nodes and connections. Some common examples include:

• Hierarchical relationships: These are relationships where one node has authority or control over another node. For instance, in an organizational chart, a manager may have hierarchical relationships with their team members.
• Association relationships: These are connections between nodes based on some shared characteristics or attributes. For example, a person and their occupation may have an association relationship.
• Network relationships: These involve multiple nodes that are connected in a non-hierarchical way. A social network is a prime example of this type of relationship, where individuals can be connected to each other in various ways through friendships, mutual interests, etc.
• Sequential relationships: These connect nodes based on a specific order or sequence, such as steps in a recipe or stages of a project.
• Causal relationships: These capture cause-and-effect connections between nodes, where one node directly influences another—commonly seen in scientific research and data.

Examples of various relationships: Albert Einstein invented the theory of relativity; Apple Inc. is headquartered in Cupertino.

3. Attributes (properties)

Attributes or properties are characteristics that describe a node. They provide additional information and context to the node and help distinguish it from other nodes in the network. Attributes can be numerical, categorical, or text-based.

Some common examples of attributes include:

• Age: The age of a person or object.
• Gender: The biological sex of a person.
• Income: The amount of money a person earns.
• Education level: The highest level of education completed by a person.
• Product category: A specific type or category of product.
• Location: The physical location of an individual, organization, or event.
• Relationship status: The marital status or relationship status of an individual.

In addition to these common attributes, there are also more specialized types that may be relevant in certain contexts. For example:

• Health-related attributes: These include things like blood pressure, cholesterol levels, and body mass index (BMI), which are often used in healthcare settings.
• Job-related attributes: These could include job title, industry, salary, years of experience, and other details that are relevant to a person's career.
• Financial attributes: These could include income, debt, credit score, assets, and other financial information that may be important in financial planning or banking.
• Location-based attributes: These can include geographical location, time zone, climate data, and other information related to a person's physical whereabouts.

Examples: For "Albert Einstein," an attribute could be [birthdate: March 14, 1879].

4. Ontologies & schema

A schema defines the types of entities, relationships, and attributes that exist in the graph, often using ontologies for more formal definitions. Ontologies ensure consistency and enable complex reasoning.

Open standard ontologies enable interoperability. Some popular ontologies used in knowledge graphs include:

• RDF Schema (RDFS): a basic ontology language that allows defining classes, properties, and hierarchies of entities.
• Web Ontology Language (OWL): a more expressive ontology language that enables complex relationships between entities and supports reasoning and inference capabilities.
• Schema.org: an open community initiative to create a common schema for structured data on the internet. It includes terminology for describing things like people, organizations, events, products, recipes, reviews, etc.

Ontologies are useful for knowledge graphs because they provide a shared understanding of the data and enable interoperability across different systems. They also help with data integration by mapping different vocabularies to a common schema.

However, reasoning and inference in knowledge graphs depend not just on expressive ontologies, but also on inference engines or reasoning tools. Ontologies provide structure and semantics, but actual reasoning requires dedicated software components to draw new conclusions from the graph’s data

Importantly, not all knowledge graphs use formal ontologies — some are schema-less or use lightweight schemas only.

Components and types of Knowledge Graphs

Knowledge graphs come in various forms, tailored to different needs and data environments. RDF triple stores and property graphs are common frameworks for building knowledge graphs

1. Triple stores (RDF)

Triple stores, also known as RDF stores or graph databases, are the most common type of knowledge graph. RDF triple stores are based on subject-predicate-object triples (e.g., SPARQL for querying).

They store data in a triple format, consisting of subject-predicate-object statements. This structure makes it easy to represent relationships between different entities and allows for efficient querying.

2. Property graphs

Property graphs allow richer properties on nodes and edges to represent entities and relationships between them (e.g., Cypher for querying).

Unlike triple stores, property graphs allow for more complex relationships by supporting multiple edge types and properties on both nodes and edges.

The role of taxonomies

Taxonomies are hierarchical structures that define categories and subcategories within a domain. These are not technically a type of knowledge graph but are often used as a hierarchical component within knowledge graphs.

Taxonomies are commonly used in e-commerce sites to classify products into different categories, making it easier for customers to browse and search for specific items. Taxonomies can also be used in knowledge graphs to group related entities together.

Benefits of using knowledge graphs

Knowledge graphs carry unique strengths that make them stand out from traditional databases and data management solutions. Let's look at some of them below:

1. Enhanced data integration

Knowledge graphs break down data silos by connecting disparate sources based on meaning, not just structure. This enables richer analytics and insights.

Rather than relying on pre-defined data relationships, knowledge graphs allow for the discovery of new connections and patterns within the data.

2. Improved search and discovery

As knowledge graphs are semantic, they allow for smarter search. Queries return related information even when exact keywords aren't used, taking context into account. This results in more accurate and relevant search results, leading to improved discovery of information.

3. Better reasoning and machine learning

Knowledge graphs provide structured, interrelated data ideal for powering AI applications. They support reasoning (inference engines can answer complex questions) and improve the quality of machine learning models by providing valuable training contexts.

They enable applications such as Retrieval-Augmented Generation (RAG). RAGs are particularly useful for machine learning because they can draw complex relationships from large datasets, helping AI systems to reason like humans. RAGs can use various sources for external knowledge, of which knowledge graphs are one option, alongside document stores and databases.

Knowledge graphs support reasoning and can enhance ML. For example, a chatbot powered by a knowledge graph can understand user queries better and provide more accurate responses by using its reasoning capabilities to analyze the context of the question and find relevant information from multiple interconnected sources.

However, most real-world systems still rely heavily on pattern recognition; semantic reasoning is challenging at scale.

Challenges and considerations

While knowledge graphs offer significant benefits, organizations must address several challenges to use them effectively. Common challenges include data quality and curation, scalability and maintenance, entity resolution, integration across sources, and keeping information up to date.

Additionally, privacy, security, and bias are important considerations, especially when sensitive or personal data is involved.

Applications of knowledge graphs

From consumer tech to enterprise solutions, knowledge graphs are making an impact.

• Search engines: Google’s Knowledge Graph is the most common example. It enables direct answers in search results (the "Knowledge Panel") and support for voice assistants.
• Recommender systems: Knowledge graphs are used to model preferences, items, and user interactions, allowing for personalized recommendations on platforms like Amazon, Netflix, and Spotify.
• Healthcare and life sciences: Medical research networks and clinical decision support tools use knowledge graphs to connect symptoms, treatments, and outcomes, helping clinicians and researchers uncover insights.
• Fraud detection: Banks and insurers use knowledge graphs to map out relationships between people, accounts, and transactions, detecting suspicious activities that traditional systems might miss.
• Knowledge management in enterprises: Large organizations deploy enterprise knowledge graphs to consolidate internal knowledge, support employee onboarding, enable project collaboration, and prevent information loss.

Knowledge graphs for AI and data integration

Knowledge graphs are key elements in AI applications and can't be ignored in the development of sophisticated systems. They have opened up new possibilities for data integration, information retrieval, and semantic reasoning. With the rapid advancements in AI technology, knowledge graphs will continue to play a crucial role in many industries and domains